iso27diy-corp/Corpus/Various/LLMs and Vibe Coding/Instruct an LLM on available tools.md at main

Provide the LLM with a list of tools, each including:
- A name (identifier)
- A description (explains when to use it)
- A schema (usually JSON) describing input parameters and their types 8 9.

2. Inform the LLM via Prompting

When sending a user query, include the tool definitions in the prompt or system message. This allows the LLM to understand what tools are available and how to use them 8 9.

3. Tool Calling Process

The LLM receives the user prompt and tool list.
It decides if a tool is needed, selects the appropriate one, and formats a tool call (often as a structured JSON object with arguments) 8 9.
Your application receives this tool call, executes the corresponding function or API, and returns the result to the LLM.
The LLM integrates the tool’s output into its next response, continuing the conversation naturally 8 9.

4. Training or Fine-Tuning (Optional)

For more advanced usage, you can fine-tune the LLM with example data showing how to use each tool, or use models already trained for tool use (like some recent open-weight models) 6 9.

Summary Table

Step	Action
Define tools	Name, description, input schema
Prompt LLM	Include tool definitions in prompt/system message
Tool call & execution	LLM outputs tool call; app executes it and returns result
Integrate response	LLM uses tool output in conversation
(Optional) Fine-tuning	Train LLM on tool usage examples for better performance

This approach enables LLMs to use external tools flexibly and accurately within applications 8 9 6.

I’m using Anything LLM. How can I enable a model to run shell scripts?GitHub

[

[FEAT]: Shell/Script invocation block for flow builder #3451 - GitHub

](https://github.com/Mintplex-Labs/anything-llm/issues/3451)reddit.com

[

Can I integrate my local LLM to enable it to run system commands...

](https://www.reddit.com/r/LocalLLaMA/comments/14cuhmm/can_i_integrate_my_local_llm_to_enable_it_to_run/)GitHub

[

how.sh - Create and execute shell commands using LLM - GitHub

](https://github.com/regnull/how.sh)anythingllm

[

AnythingLLM | The all-in-one AI application for everyone

](https://anythingllm.com/)

To enable shell script execution in AnythingLLM, you can implement custom agent skills through these methods:

1. Custom Agent Skill Development

Create a plugin.json file defining your shell script entrypoint:

json
{
  "name": "shell_runner",
  "description": "Execute shell commands",
  "entrypoint": "./handler.js",
  "permissions": ["shell"]
}

Develop a handler file (e.g., handler.js) using Node.js' child_process module 1 7:

javascript
const { exec } = require('child_process');
module.exports = async ({ command }) => {
  return new Promise((resolve, reject) => {
    exec(command, (error, stdout, stderr) => {
      if (error) reject(error);
      resolve(stdout || stderr);
    });
  });
};

2. Security Considerations

Restrict allowed commands through allow-listing in your handler
Implement user permission tiers for script execution
Use environment sandboxing for dangerous operations 6

3. Integration Approaches

LangChain Agent Method (shown in tutorial 5):

python
from langchain.agents import initialize_agent
from langchain.tools import ShellTool
shell_tool = ShellTool()
agent = initialize_agent([shell_tool], llm, agent="zero-shot-react-description")

Ollama Local Model Integration 3:

bash
ollama pull deepseek-coder-v2
llm -m ollama/deepseek-coder-v2 "convert all .txt files to .md" > convert.sh

4. Execution Flow

User requests shell operation through chat interface
LLM processes request and generates structured tool call
Handler executes the command via Node.js exec() 1 7
Output is returned to LLM for response formatting

Example Use Case (from GitHub issue 1):

bash
# plugin.json entry
"entrypoint": "python -m browser_automation.py"

Limitations & Risks

Requires careful input sanitization to prevent injection attacks
Windows support limited due to shell differences 5
Execution environment must match user's system capabilities 2 6

For production use, consider implementing:

Command validation middleware
Output sanitization filters
Session-based execution isolation
Activity logging for audit trails 6 7

What are the best methods to integrate local executables with Anything LLM for automationsemaphoreci

[

6 Ways to Run LLMs Locally (also how to use HuggingFace)

](https://semaphoreci.com/blog/local-llm)Stream

[

The 6 Best LLM Tools To Run Models Locally - GetStream.io

](https://getstream.io/blog/best-local-llm-tools/)apidog

[

The 5 Best LLM Tools To Run Models Locally - Apidog

](https://apidog.com/blog/top-llm-local-tools)anythingllm

[

AnythingLLM | The all-in-one AI application for everyone

](https://anythingllm.com/)

To integrate local executables with AnythingLLM for automation, use these methods:

1. Custom Plugin Development

Create executable-wrapping plugins using AnythingLLM's extensible architecture:

javascript
// plugin.json
{
  "name": "pdf_converter",
  "description": "Converts DOCX to PDF using LibreOffice",
  "entrypoint": "./convert.js",
  "permissions": ["file_system"]
}

// convert.js
const { execSync } = require('child_process');

module.exports = async ({ inputPath }) => {
  try {
    execSync(\`soffice --convert-to pdf "${inputPath}" --headless\`);
    return { success: true };
  } catch (error) {
    return { error: error.message };
  }
};

Key Features:

Direct CLI execution via Node.js child_process 5 8
File system access controls through permission tiers
Error handling integration with LLM feedback loops

2. LangChain Agent Integration

Leverage LangChain's tool ecosystem for executable orchestration:

python
from langchain.agents import initialize_agent
from langchain.tools import Tool

def run_executable(command):
    import subprocess
    result = subprocess.run(command, shell=True, capture_output=True)
    return result.stdout.decode()

agent = initialize_agent(
    tools=[Tool(
        name="LocalExec",
        func=run_executable,
        description="Runs local system commands"
    )],
    llm=anything_llm_instance,
    agent="structured-chat-zero-shot-react-description"
)

Advantages:

Native integration with AnythingLLM's chat interface 4 7
Automatic command validation through LangChain's schema system
Multi-step workflow automation capabilities

3. Llamafile Binary Bundling

Package executables with LLMs using Mozilla's Llamafile:

bash
# Combine executable and LLM into single binary
llamafile-convert --exec my_script.py --model mistral-7b.gguf

Benefits:

Self-contained deployment without dependency conflicts 2 6
Cross-platform support (Windows/Linux/macOS)
Direct memory sharing between executable and LLM 6

Security Implementation

Measure	Implementation Example
Input Sanitization	Regex whitelist for allowed CLI parameters
Permission Tiers	User roles with executable access levels
Sandboxing	Docker containers for risky operations
Audit Logging	JSON logs of all executed commands

Use Case Workflow

User requests "Convert quarterly_report.docx to PDF"
LLM triggers pdf_converter plugin via structured JSON 5
Handler executes LibreOffice CLI in Docker sandbox
Output PDF path returned to LLM for user sharing

Best Practices:

Use absolute paths for local executables
Implement timeout guards for long-running processes
Test with --dry-run flags before production deployment 5 8

For complex automation, combine methods 2 and 3 using Llamafile-packaged tools controlled through LangChain agents 6 7.

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1. Custom Plugin Development

2. LangChain Agent Integration

3. Llamafile Binary Bundling

Security Implementation

Use Case Workflow

11 KiB

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